Retailers recognize the value of targeting offers to specific customers. Through the use of frequent shopper cards, many retailers are collecting and recording the purchase activity of their customers. However, it has proven difficult for retailers to effectively leverage their data to deliver meaningful incentives to their customers.
A major challenge that retailers face is the overwhelming amount of purchase data and the large number of items purchased. A common method of leveraging frequent shopper data is to establish an incentive and then deliver the incentive to customers that “qualify” for the offer based on specified purchase criteria. For example, a coupon for savings on a particular brand of peanut butter might be targeted to all customers purchasing peanut butter in the last year, but never purchasing that particular brand. This type of process works by segmenting or clustering customers based on the qualification criteria, but does not achieve true personalization of the offers.
True personalization can be achieved by having a collection of multiple offers with the optimal offers selected to be presented to each customer. Each offer or incentive can be measured or scored, with the best offers being selected for each customer. In one application of the invention, the offer set is the existing promotional specials contained in a retailer's weekly ad. By using existing ad specials, there is no need for the retailer or manufacturers to come up with incremental promotional dollars to fund the customer incentive.
What makes the invention especially powerful is that it uses a “consumer-centric” evaluation method to determine which offers should be presented to each customer. This consumer-centric evaluation quantifies the relevance of each ad item for each customer, such that personalized ads to customers achieve maximum results.
Relevance—Promo Groups
Certain existing programs claim to target customers “based on their prior purchase history.” In practice, however, all that is done is to determine if the customer shops within the category or has purchased a particular item. This segmentation or clustering method can determine which offers should be presented to a customer (which offers they “qualify for”), but it is ineffective at prioritizing among the offers or evaluating the relevance of an advertised item.
The present method uses a unique, consumer-centric approach by designating relevance items (a “promo group”) for each feature item contained in the advertisement. The promo group is a collection of items including one or more non-ad items that, if purchased, indicate an interest in the feature ad item. These promo groups are created based on an understanding of the customers' uses of an item, and therefore cannot be created using standard category and brand definitions.
A key added benefit of the invention is that it removes a major logistical issue related to creating personalized ads. In the present system, retailers do not need to provide their ad data files (price files) every week. It also allows for more lead time since the ad layouts (for the print versions) are finalized at least 2 weeks prior to the ad drop. The ad pricing files are sometimes modified much closer to the actual ad run date.
The invention also allows a direct comparison with competitor ads because the same printed ad visuals can be evaluated for the competitor. If the pricing file is used, there is no way to collect similar data for competitor's ads. By creating promo groups according to the present invention, it is possible to establish a standardized measure of the value of any ad against any customer group. Outside of the present invention, there are currently no methods employed in the industry to score or measure the value of an ad in a standardized and consumer-centric way.